Electron Markets (2016) 26:73–81
DOI 10.1007/s12525-015-0211-0
RESEARCH PAPER
Can privacy concerns for insurance of connected cars
be compensated?
Sebastian Derikx 1 & Mark de Reuver 1 & Maarten Kroesen 1
Received: 23 July 2015 / Accepted: 11 December 2015 / Published online: 30 December 2015
# The Author(s) 2015. This article is published with open access at Springerlink.com
Abstract Internet-of-things technologies enable service providers such as insurance companies to collect vast amounts of
privacy-sensitive data on car drivers. This paper studies
whether and how privacy concerns of car owners can be compensated by offering monetary benefits. We study the case of
usage based car insurance services for which the insurance fee
is adapted to measured mileage and driving behaviour. A conjoint experiment shows that consumers prefer their current
insurance products to usage based car insurance. However,
when offered a minor financial compensation, they are willing
to give up their privacy to car insurers. Consumers find privacy of behaviour and action more valuable than privacy of
location and space. The study is a first to compare different
forms of privacy in the acceptance of connected car services.
Hereby, we contribute to more fine-grained understanding of
privacy concerns in the acceptance of digital services, which
will become more important in the upcoming Internet-ofthings era.
Keywords Privacy . Mobile services . Insurance .
Internet-of-things . Connected cars . E-mobility
JEL classification L86
Responsible Editor: Hans-Dieter Zimmermann
* Mark de Reuver
[email protected]
1
Delft University of Technology,
Jaffalaan 5, 2628 BX Delft, The Netherlands
Introduction
As mobile and sensor technologies are becoming omnipresent, the vision of connected cars is unfolding (Fleisch et al.
2014; Gerla et al. 2014). By connecting cars to the Internet,
vast amounts of data can be collected on mileage and driving
behaviour (Paefgen et al. 2012). Service providers can utilize
such data to offer value-added services like traffic safety, vehicle diagnostics, preventive maintenance and advanced real
time navigation (Leminen et al. 2012). Connected cars also
enable actors in the car industry to engage new methods for
customer relationship management, (proximity) marketing
and after-sales services (Tenghong et al. 2012).
However, collecting vast amounts of data from connected
cars creates privacy and ethical hazards. In general, privacy
concerns negatively affect the intention to use digital services
(Malhotra et al. 2004; Miyazaki and Fernandez 2001). Service
providers can compensate privacy concerns by offering convenience or monetary rewards as has been shown for ecommerce services (Hann et al. 2007; Laudon 1996; Li et al.
2010). However, sensitivity of disclosed personal data will be
substantially higher for connected car services than traditional
electronic services as highly detailed habits and mobility patterns can be inferred. Since sensitivity of disclosed personal
data has a significant positive effect on related privacy concerns (Bansal and Gefen 2010), the question arises whether
and how such elevated privacy concerns can still be compensated by service providers.
This paper studies if and how privacy concerns for connected car services can be compensated financially. We study
this issue through a discrete choice experiment in which the
buy-off value of different types of privacy risks is evaluated.
We define privacy as Ban interest that individuals have in
sustaining a ‘personal space’ free from interference by other
people and organizations^ (Clarke 1999). As a case to study
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this issue, we focus on usage based insurance services (Handel
et al. 2014). Insurance services are especially relevant as privacy concerns regarding the insurance industry and its online
platforms are already high. Specifically, we consider usage
based insurance services for which the insurance fee is based
on actual car use. Differentiating insurance fees based on car
use is relevant since damage risks are correlated to the amount
of driven kilometres (Vonk et al. 2003) as well as driving
behavior (Lajunen et al. 1997). Usage based insurance services are starting to emerge on the market that utilize not only
GPS-data but also motion sensors to measure car acceleration/
deceleration and driving behavior. Usage based insurance services are a suitable research object since they raise privacy
concerns (Troncoso et al. 2011).
Section 2 provides the background of the study, followed
by an overview of the mobile insurance domain in Section 3.
Section 4 provides the method, followed by results in
Section 5. Section 6 discusses the findings and concludes
the paper.
Theoretical background
Privacy as an interest
Academic discourse on privacy takes place in different
schools with each their own assumptions (Bennett 1992). Initial notions on privacy have been made over 2000 years ago,
most notably Aristotle’s distinction between the public sphere
of political activity and the private sphere associated with
family and domestic life. Innovations like photography and
newspapers instigated modern debates on privacy in the last
part of the 19th century.
Traditionally, privacy has been conceptualized as a
right to control over information about oneself. Westin
(1968) defines privacy as the ability of individuals to
determine for themselves when, how, and to what extent
information is communicated to others. Westin further
elaborates on four states (solitude, intimacy, anonymity
and reserve) and four functions of privacy (personal autonomy, emotional release, self-evaluation, and limited
and protected communication). The four states indicate
how different size units (individual and groups) are involved in the privacy phenomena and how the setting
influences the concept of privacy. The four functions
describe multiple modes to act regarding privacy, which
may vary over time and per setting. Altman (1975) elaborated further on this non-monotonic view on privacy to
explain why people sometimes prefer to be left alone but
at other times like to engage with others. He defines
privacy as a dialectic and dynamic boundary regulation
process which allows a selective control of access to the
self or to one’s group.
S. Derikx et al.
While the previous conceptualizations assume privacy is a
right, an alternative view is to consider privacy as a moral
value. In this view, privacy is defined as a condition of not
having undocumented personal information known or possessed by others (Parent 1983).
More recently, utilitarianists have conceptualized privacy
as an interest rather than an absolute right. Clarke (1999) considers privacy as a thing that people like to have. Clarke
(1999) defined privacy as Ban interest that individuals have
in sustaining a ‘personal space’ free from interference by other
people and organizations^.
Many more privacy theories exist in different scientific
disciplines. For instance, Posner (1981) conceptualizes privacy from an economic perspective as market failure because it
results in information asymmetry. Thomson (1975) describes
privacy as redundant since privacy rights overlap with constitutional rights as property rights and rights to bodily security.
Many more (sub)theories and views on privacy exists
(Burgoon et al. 1989; Paine et al. 2007).
This study will follow the utilitarian view of privacy as an
interest, since this implies that privacy can be given up. As
long as the benefits of the service exceed related sacrifices,
users will be persuaded to give up their privacy. Similar conceptualizations dominate related work on privacy concerns in
e-commerce (Chorppath and Alpcan 2013; Dinev and Hart
2006; Hann et al. 2007; Li et al. 2010).
Central to the definition of privacy is the issue of privacy
concern (Westin 1967). Privacy concerns are defined here as
the deviation between required privacy interests and satisfied
privacy interests.
Conceptualizing privacy in the Internet-of-things era
Privacy interests can be affected by various activities, i.e. (1)
information collection, (2) information processing, (3), information dissemination, and (4) invasion (Solove 2006).
Assuming that privacy is an interest, Clarke (1999) suggests various types of privacy that may be relevant. Clarke
defined four categories of privacy, including privacy of the
person, privacy of personal data, privacy of personal
behavior and privacy of personal communication. Finn et al.
(2013) argue that these four types of privacy do not cover
potential privacy issues of recent technological advances.
Technologies such as whole body image scanners, RFIDenabled travel documents, unmanned aerial vehicles, advanced DNA enhancements, second-generation biometrics
and connect mobile services raise additional privacy issues.
Therefore, Finn et al. (2013) expanded Clarke’s categorization
to seven types of privacy: privacy of the person, privacy of
behaviour and action, privacy of personal communication,
privacy of data and image, privacy of thoughts and feelings,
privacy of location and space, and privacy of association.
Compensating privacy concerns for insurance of connected cars
Mobile insurance services especially affect privacy of behaviour and action, data and image, and location and space.
Privacy of behaviour and action can be affected as data from
mobile devices allow identifying travel activities. Especially
when combining positioning data from mobile devices, GPS
chips and social media, extensive information on one’s behaviour and action can be generated. Privacy of data and image is
affected as mobile insurance will typically require personal
data to be shared. Privacy of location and space is especially
impacted by tracking technologies in mobile phones and cars.
Usage based insurance products typically require sharing location information with insurers. Almost all connected devices, even without GPS-sensors, provide detailed information on their location IP addresses, WiFi hotspots and router
information.
Privacy and monetary compensation
Privacy is generally seen as a value that stimulates individual
freedom and social development (Solove 2006). Based on a
review of existing studies, Paine et al. (2007) show that the
general public is increasingly concerned about their online
privacy and willing to take countermeasures. At the same
time, studies show that most consumers consider disclosing
personal information as an integral part of modern life, necessary to obtain products and services (Preibusch 2013). As
such, individuals do consider a utilitarian trade-off between
perceived benefits of online services and sacrifices of disclosing personal information.
Disclosure of personal information generally results in elevated privacy concerns (Bansal and Gefen 2010). Various
empirical studies show that elevated privacy concerns negatively affect the intention to use online and mobile services
(Malhotra et al. 2004; Miyazaki and Fernandez 2001).
Laufer and Wolfe (1977) suggest that individuals perform a
Bcalculus of behavior^ to assess the consequences of providing personal information. On the basis of this theoretical construct, individuals consider a trade-off between perceived
benefits and sacrifices of disclosing personal information.
This implies that unavoidable privacy concerns, associated
with the use of mobile insurance, have to be compensated in
order to persuade consumers to adopt. Hann et al. (2007) state
that providers can mitigate the negative effect of privacy concerns on intention to use in two ways: (1) by offering privacy
policies regarding the handling and use of personal information and (2) by offering benefits such as monetary rewards or
convenience. The latter type of compensating benefits have
been further operationalized by Li et al. (2010) into expected
monetary benefits and perceived usefulness.
Laudon (1996) argues that personal information is a commodity that can be priced and exchanged for monetary
benefits. Further research by Jen et al. (2013) showed that
the expected monetary benefits have a positive influence on
75
intention to use electronic services. Hereby monetary benefits
could be achieved through a discount on existing services or
direct pay-outs (Jen et al. 2013).
Mobile insurance
Insurance industry
Insurance is defined as the equitable transfer of the risk of a loss,
from one entity to another, in exchange for a payment (Dorfman
and Cather 2012). In practice this comes down to a contract
whereby in consideration of a premium, the insurance company
agrees to cover the insured subject to agreed limit, in the event of
a loss (Ibiwoye 2011). Insurance works by pooling risks: while
assessing risks for one individual person involves high uncertainties, fairly precise risks can be estimated for large groups of
people (Smith and Kane 1994). A more precise expectation of
risk events enables more precise premium calculations, enabling
insurers to offer more competitive prices (Mackenzie 2014). By
pooling risk, the concept of insurance provides both benefits for
consumers as insurers. From a consumer perspective, insurance
serves financial security and stability since future uncertainties
are covered. For insurance companies, insurances are their driving element of business contributing to their profit. In this paper,
we focus on consumer rather than corporate insurance products,
and on non-life rather than life insurance.
Although the insurance industry is not recognized for being
innovative, advanced mobile technologies offer great opportunities (Berdak and Carney 2014). Insurance companies earn
revenues from insurance premiums collected from insurance
takers but also earn income from return on investments financed by pooled insurance premiums. Insurance companies
expenses include incurred losses from paid and reserved claims
as well as underwriting expenses resulting from selling and
administering insurance policies. By optimizing and automating insurance processes, such as sales-, contract- and claim
activities, efficiency gains could be achieved and underwriting
expenses could be minimized (Berdak and Carney 2014). However, mobile technologies may also help insurers to reduce their
incurred losses as data on behavior of insurance takers helps
insurers to estimate risks more accurately. In addition, insurers
could feed back data to insurance takers in order to enhance risk
awareness, which subsequently may reduce incurred losses.
What is mobile insurance?
A wide range of mobile insurance services exist on the market.
We explore a variety of sources including journals, theses,
conference proceedings, reports, newspapers, business publications, market scans, industry expert reviews and the Internet.
Search terms employed were: innovative, mobile, connected,
future, wearable, location, context, insurance, service, home,
76
S. Derikx et al.
car, health, service, product, application, innovation and discount. Instances of mobile insurance services were added to a
long-list as long as they had insurance elements, utilized some
form of context information and were distinct from services
already on the long-list. We stopped adding services to the
long-list when no new service concepts were elicited. The ultimate long-list contains 53 mobile insurance services that were
composed through 5 days of full-time work effort.
Next, we categorized services based on their main functionality. The categorization was validated and minor refinements were made through individual interviews as well as a
workshop with experts within a large consultancy firm that
have expertise on digital insurance and insurance innovation.
The resulting categorization is displayed in Table 1.
In this study, we focus on usage based insurance services
for which the insurance fee is based on actual car-use.
the Netherlands (Schoonhoven) in October 2014. To maximize the chance of finding private car owners, the survey
was carried out on a Friday. After approval of the ferry service,
car owners were approached to complete the questionnaire.
Hardcopy questionnaire results were imputed into a
spreadsheet.
Sixty respondents completed the questionnaire, of which
five were omitted due to missing data. The resulting sample is
representative in terms of gender (48 % male compared to
49 % in the target population) and car use (55 km per day
on average compared to 37 km in the target population). The
sample is biased towards highly educated (51 % higher education compared to 34 % in the target population) and younger
people (34 % between 18 and 25 compared to 13 % in the
target population).
Measurement instrument
Method
We conduct a discrete choice experiment to evaluate the interplay of privacy concerns, monetary compensation and the intention to use usage based insurance services. Conjoint analysis
is a statistical approach, often used in market research to determine customer preferences (Green et al. 2001; Hensher et al.
2005; Louviere et al. 2000) . Based on implicit trade-offs, perceived utilities by the respondents can be estimated per profile
characteristic. By involving financial dimensions in the composition of these profiles, the willingness to pay might also be
an output of the conjoint analysis (Hensher et al. 2005). We use
stated-choice model (Louviere et al. 2000) rather than ratingbased conjoint analysis since in reality consumers also make a
discrete choice between multiple car insurance packages.
Sample
The population of interest comprises all Dutch private car
owners. The survey was carried out at a car ferry service in
Table 1
In order to value individuals’ privacy in monetary units, the
three relevant forms of privacy identified in Section 2 are
operationalized into attributes, see Table 2. Hereby, the attribute levels are composed in such a way that one level involves
privacy harm and the other level involves no privacy harm.
Operationalization of the privacy types is done by building
upon examples of mobile insurance products that are emerging on the market currently. As such, operationalization is as
close to reality of respondents as possible, which contributes
to the external validity of the study. As our main objective is to
generalize to the three dimensions of privacy rather than to
traits of individual respondents, we omit generic constructs
such as a person’s generic concern over privacy.
Privacy of location and space is operationalized into the
attribute Kilometer registration, which is an important input
for usage based car insurance. The insurer can measure the
number of kilometers driven automatically through GPS
tracking, which harms privacy of location and space. Alternatively, the consumer could register the number of kilometers
Mobile insurance product categorization
Mobile insurance category
Usage based insurance;
Behavioral rewarding;
Up-to-date insurance package;
Explanation
With a usage-based insurance premium, consumers pay only premium for actual use of their insurance.
By rewarding customers for less risky behavior, the insurer is trying to reduce the risk of accidents.
By using personal (context sensitive) information of consumers, relevant personalized insurance
products could be provided.
Preventative information services; Consumer context information offers insurers the opportunity to provide consumers with relevant context related
preventative information.
Accident detection & prevention; By detecting (potential) accidents as early as possible, damages could be prevented and minimized.
Mobile accessibility;
Mobile technologies facilitate a communication channel for sales and services.
Personal dashboards;
By measuring individual behavior, insight could be provided in risk profiles of consumers to increase risk awareness.
Additional informative services; Context sensitive information offers opportunities for several semi-insurance services.
Compensating privacy concerns for insurance of connected cars
Table 2
77
Conjoint attributes and levels
Privacy type (construct)
Attribute
Level 1 (no privacy harm)
Level 2 (privacy harm)
Privacy of location and space
Kilometer registration
Manual (web platform)
Automatic (in-car GPS)
Privacy of behavior and action
Registration road behavior
No
Yes (in-car motion sensor)
Privacy of data and image
Additional insurance offerings
Third party advertisement
No
No
Yes
Yes
driven manually through a website, which does not harm privacy of location and space.
Privacy of behavior and action is operationalized into the
attribute Registration road behavior. Driving behavior could
be measured automatically through an in-car motion (G-force)
sensor that registers acceleration, deceleration and abrupt
steering movements. By doing so, insurers gain in-depth insights in the actual user behavior which harms privacy of
behavior and action.
Privacy of data and image is operationalized into the reuse
of data generated by a usage based insurance service for secondary purposes. The attribute Additional insurance offerings
refers to the insurer sending personalized offerings and promotions based on the data collected about the user. The sending of promotions by parties other than the insurer is referred
to as Third party advertisement. As both options reuse data
provided by the user for secondary purposes, they both negatively affect privacy of data and image.
The results of the conjoint analysis will provide the utility
that participants derive from every attribute level. By adding a
fifth attribute, these utilities can be converted into monetary
compensation level, thereby eliciting the buy-off value of privacy. This fifth attribute Relative consumer saving is defined
as the discount consumers will receive when adopting the
usage based insurance policy. To analyze potential nonlinear effects, three attribute levels are included: 0, 10 or 20
euros discount. The level of discount is considered appropriate
considering the average monthly fee of all-risk Dutch car insurance policies equals €34.
Based on the defined attributes, choice-sets are composed
in which respondents compare two alternative usage based
insurance options. In addition, respondents were asked whether they prefer the proposed insurance policy or their current
policy. The latter option has been considered as the ‘none of
the choice sets’ option, which is a common feature in discrete
conjoint analysis. A balanced composition of twelve choicesets and related attribute levels was generated using Ngene
software. Based on the choice-sets and defined attributes, a
questionnaire was designed and subsequently pretested with
three participants.
The consistency of the model results was verified randomly
dividing all respondents’ choice-preferences in two equal
parts and running the analysis individually for both parts.
All estimated coefficients in the sub-groups have the same
direction as in the full model, and deviations are generally
acceptable.
Finally, the uniqueness of each attribute was assessed by
computing the correlations between coefficients. All correlations were lower than 0.80, which indicates that the model was
able to unique identity the influences of the included attributes
(Hensher et al. 2005).
Results
A discrete choice model is estimated to analyze the choice
behavior of the respondents (Ben-Akiva and Lerman 1985).
The software package Biogeme is used to this end (Bierlaire
2003). The dataset includes all predefined choice-sets and all
respondents’ choices from the questionnaire. The model-file
includes a syntax program language to provide instructions to
the Biogeme engine.
Table 3 provides the part worth utilities of the attributes,
which are calculated based on the parameter estimates. T-tests
are performed to test the significance of the parameter estimates.
The results indicate that all parameters (and therefore all attributes) are statistically significant. Relative consumer saving has
the highest importance: 65 % of a choice for usage based insurance depends on the discount offered. The residual importance
is almost equally distributed over the other attributes which implies a balance willingness to pay for all attributes.
Table 3 also shows a significant disutility of 1.21 compared
to the current car insurance policy. In other words, respondents derive a structural disutility from usage based insurance
services of 1.21.
Next, we transform utility levels to buy-off values using the
Relative consumer saving attribute. As 20€ savings corresponds to 2.536 utility points (see Table 4), 1 utility point
equals 7.89€. Based on this valuation, the structural disutility
of usage based insurance equals €9.54, i.e. a buy-off value of
€9.54 per month should be offered for consumers to switch to
usage based insurance services.
Table 4 presents the buy-off values for each form of privacy
harm. In the table, the utility is calculated in a buy-off value
using the attribute Monetary compensation.
Table 4 shows that all buy-off values are in a similar range.
Privacy of behavior and actions has a slightly higher buy-off
value, equaling €2.98 per month.
78
S. Derikx et al.
Table 3 Part worth utilities
Attribute
Attribute level
Part worth utility
Range
Importance
Rank
Kilometer registration
Manual
Automatic
No
Yes
0
−0.288†
0
−0.378*
0.288
7.34 %
5
0.378
9.64 %
2
No
Yes
No
Yes
€0
€ 10
0
0.369*
0
−0.351*
−1.42*
0.304
0.369
9.41 %
3
0.351
8.95 %
4
2.536
64.66 %
1
€ 20
1.116
Registration road behavior
Additional insurance offerings
Third party advertisement
Relative consumer saving
−1.21
Constant
†
p < .10; * p < .05
Regarding the privacy of data and image two buy-off
values are determined, relative to the internal and external
reuse of personal data. Respondents are willing to sell their
personal data for third party advertisements if they receive a
financial compensation of €2.77 per month. Strikingly, to receive relevant personalized promotions from the insurance
company itself, respondents are willing to pay a monthly fee
equaling €2.91.
Next, we explore moderating effects of demographics on the
utilities, which is especially relevant considering the sampling
bias towards younger and higher educated people. The main
purpose of conjoint analysis is make generalizable claims regarding the conjoint attributes rather than the respondent characteristics. Despite this, we did explore moderating effects of
demographic variables since our sample is slightly biased towards younger and higher educated people. By comparing multiple groups, we can assess whether this bias is a serious threat
to the validity of our findings. Doing so requires recoding categorical and ratio variables into dichotomous scales, which
requires establishing cut-off points. To distinguish age groups,
we used the median as a cut-off value. By using the median cutoff, both groups have similar size which increases the chances
of finding significant differences. For distinguishing education
groups, we use the common distinction in the Netherlands of
high (i.e. bachelor or master degree) and low education levels.
To compare heavy and low car users, we used the somewhat
arbitrary cut-off point of 30,000 km per year.
Table 4
We reran the conjoint analysis for the demographic subgroups. Table 5 shows that demographics have considerable
effect on the utilities in the conjoint model. For instance, highly educated respondents only require €4.42 to adopt usage
based insurance, while lower educated respondents demand
€21.33. Moreover, respondents driving more than 30,000 km
per year require more compensation than those that drive less.
Regarding the privacy attributes, demographic groups differ only slightly. For instance, younger respondents derive
more disutility from registration of road behavior than older
people (€3.55 and €1.65 respectively). Higher educated people appear to derive more disutility with the registration of
road behavior. However, we should point out here that sample
size for the sub-groups is low and thus results can only be used
in a speculative manner.
Discussion and conclusions
Our study shows that specific privacy concerns about usage
based insurance services can be compensated by offering a
marginal monthly fee. Consumers perceive privacy of behavior and action as more valuable than privacy of location and
space. Regarding privacy of data and image, the buy-off value
depends on who exploits privacy-sensitive data. While usage
of personal data for personalized offerings from the insurer is
positively evaluated, third party advertisements have a
Conjoint utilities and buy-off value privacy
Type of privacy
Attribute
Involved attribute level
Utility
Buy-off value per month
Privacy of location and space
Privacy of behavior and action
Privacy of data and image (internal)
Privacy of data and image (external)
Kilometer registration
Registration road behavior
Additional insurance offerings
Third party advertisement
Automatic (in-car GPS)
Yes (in-car motion sensor)
Yes
Yes
−0,288†
−0,378*
0,369*
−0,351*
€2,27
€2,98
–€2,91
€2,77
†
p < .10; * p < .05
Compensating privacy concerns for insurance of connected cars
Table 5
79
Buy-off values for different demographic groups (in euros per month)
Full model Age group
Education level
Average number of kilometres per year
<41.5 (N = 27) >41.5 (N = 26) Low (N = 22) High (N = 31) <30,000 (N = 38)
>30,000 (N = 17)
Constant
Kilometer registration
9.46*
2.27
7.12*
1.51
11.29*
3.16
21.33*
2.37
4.42*
2.35
7.48*
3.14*
15.67*
−1.27
Registration road behavior
2.98*
3.55*
1.65
0.54
3.03*
2.48
1.32
−1.49
2.34
−4.21*
3.81
−6.71
3.77
−1.66
2.30
−2.39
2.93*
−2.19
−0.10
Additional insurance offerings −2.91*
Third party advertisement
2.77*
* p < .05
negative utility. One explanation may be that consumers trust
their insurer more than unnamed third parties, as institutional
trust has been shown to have an indirect effect on intention to
share privacy-sensitive data for usage-based insurance services (Kehr et al. 2015). We also observe that for instance
Dutch banks already offer personalized banking products for
many years, but that public outrage emerged when one of the
banks announced to disclose personal information for third
party advertisements. Our findings do indicate that consumers
prefer conventional car insurance policies considerably compared to usage based insurance, regardless of privacy concerns. As such, other considerations than privacy will likely
play a role in the adoption decision of consumers. For instance, unwillingness to switch in general or normative considerations of fairness in insurance policies may play a role.
We did not find considerable differences when comparing
older and younger drivers. We did find that people driving
more kilometres are less likely to accept usage based insurance, which can be explained because this group would pay a
higher fee due to the nature of the product. A striking difference is between people with a higher education degree (i.e.
bachelor or master degree) and those without as the latter
group requires a much higher financial compensation. Previous studies show that safe driving behaviour is not clearly
related to education level (Lourens et al. 1999; Shinar et al.
2001), and privacy concerns regarding Internet technologies
have been shown to be higher for higher educated people
(Sheehan 2002). As such, this finding, being only speculative
here due to low sub-sample sizes, should be explored in future
research.
The main downside of this survey is its representativeness.
Highly educated people and people in the age-interval of 18–
35 are overrepresented, and the conjoint analysis suggests that
younger and highly educated people are less concerned about
privacy risks. Another limitation is that interaction effects between the different dimensions of privacy were not included,
which could be added in future studies.
Practical implications are that privacy concerns on usagebased insurance products can be overcome by offering minor
financial compensations. Usage-based car insurance can
benefit insurers as they are able to offer fairer pricing of insurance policies based on actual usage. The data collected
through usage-based car insurance products can also be used
by insurers to reduce their incurred losses as they allow more
accurate risk estimations. Besides insurers, our finding that
privacy concerns can be overcome is also relevant for providers of road pricing or other connected car services. In addition, the study shows that consumers may actually attribute
positive utility to relevant personalized advertisements from
insurance companies based on car usage data. Insurers may
thus consider adopting more proactive marketing strategies
towards consumers based on data collected through sensors
and mobile devices. As such, usage-based car insurance products can also be used by insurers to offer more personalized
and relevant offering to their customers. However, while
usage-based insurance is technically feasible (Handel et al.
2014), earlier studies have shown that they require fundamental changes in the structure, business model and strategy of
insurers (Ohlsson et al. 2015).
In terms of operationalization, different dimensions of privacy could have been measured differently. For instance, privacy of data and image could also relate to the degree to which
users have control over who uses their data for noncommercial purposes. Moreover, if the operationalization of
privacy of data and image would have included calculation of
risk profiles and rising of rates based on driving behaviour,
higher disutility may have been found. Future studies may
include other factors that influence intention to disclose
privacy-sensitive information, such as general privacy concerns, trust in institutions and affect (Kehr et al. 2015).
This paper takes a utilitarian view on privacy and assumes
privacy concerns can be compensated financially. While this
view fits the increasingly dominant utilitarian privacy literature, we are aware that there are other privacy schools that
have differing conceptualizations and consider privacy as a
right that cannot be bargained for (e.g., Westin 1967).
The study contributes to theories on privacy by
distinguishing multiple dimensions of privacy rather than the
typically one-dimensional operationalization in literature. The
study shows that the buy-off value for privacy varies
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depending on the dimension of privacy concerned. This is
especially relevant as Internet-of-things and connected cars
concepts will involve ever more complex data to be released
which may affect different dimensions of privacy in different
ways. Self-driving autonomous cars may lead to even more
research issues regarding mobile insurance. For instance, assuming that self-driving cars will increasingly be shared
among pools of drivers (i.e. sharification), insurance policies
and related liability risks are no longer tied to cars but to
service providers or software vendors. In that scenario,
trade-offs will become even more prevalent between privacy,
added value and monetary compensation.
Acknowledgments The authors wish to thank Harry Bouwman, Arjen
Beers and Amira Mahawat Khan for constructive comments on earlier
versions of the paper, and Deloitte Consulting for giving the opportunity
to conduct part of the work. The paper also benefited from review and
comments from the 28th Bled eConference, where a preliminary version
of this paper was presented (Derikx et al. 2015).
Open Access This article is distributed under the terms of the Creative
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creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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